scholarly journals Analysis of the Driver’s Behavior Characteristics in Low Volume Freeway Interchange

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Ronghua Wang ◽  
Jiangbi Hu ◽  
Xiaoqin Zhang
Keyword(s):  
Eye Tracking ◽  
Field Experiment ◽  
Traffic Safety ◽  
Data Sets ◽  
Running Speed ◽  
Influence Area ◽  
Box Plot ◽  
Low Volume ◽  
Behavior Characteristics ◽  
Merge Point

Drivers’ behavior characteristics cannot be ignored in designing freeway interchange facilities in order to improve traffic safety. This paper conducted a field experiment in Qingyin expressway. Four freeway interchanges from K571+538 to K614+932 with relatively low volume were selected, and 12 qualified drivers, 6 car test drivers and 6 truck test drivers, were driving vehicles according to the driving program. GPS and eye-tracking instrument were employed to record running speed, real-time, running track, fixation point, and so forth. Box-plot graphs and Student’st-test were used to analyze the 12 data sets of driver’s fixation on exit guide signs. Speed-distance curves of effective 11 data sets were plotted to examine the test drivers’ behavior in diverging area and merging area. The results indicated that (1) drivers recognize the exit direction signs in 170 m–180 m advanced distance; (2) the diverging influence area is 1000 m upstream of the diverge point, and the merging influence area is 350 m downstream of the merge point; (3) NO OVERTAKING sign is recommended to be placed at 350 m upstream of the diverge point. The results can provide guidance for the design of freeway interchange facilities and management in order to improve traffic safety.

Graphs and Maps

2013 ◽  
Author(s):  
James B. Elsner ◽  
Thomas H. Jagger
Keyword(s):  
Web Site ◽  
Data Sets ◽  
Good Strategy ◽  
Sample Mean ◽  
The Third ◽  
Standard Base ◽  
Open Session ◽  
Box Plot

Graphs and maps help you reason with data. They also help you communicate results. A good graph gives you the most information in the shortest time, with the least ink in the smallest space (Tufte, 1997). In this chapter, we show you how to make graphs and maps using R. A good strategy is to follow along with an open session, typing (or copying) the code as you read. Before you begin make sure you have the following data sets available in your workspace. Do this by typing . . . > SOI = read.table("SOI.txt", header=TRUE) > NAO = read.table("NAO.txt", header=TRUE) > SST = read.table("SST.txt", header=TRUE) > A = read.table("ATL.txt", header=TRUE) > US = read.table("H.txt", header=TRUE) . . . Not all the code is shown but all is available on our Web site. It is easy to make a graph. Here we provide guidance to help you make informative graphs. It is a tutorial on how to create publishable figures from your data. In R you have several choices. With the standard (base) graphics environment, you can produce a variety of plots with fine details. Most of the figures in this book use the standard graphics environment. The grid graphics environment is even more flexible. It allows you to design complex layouts with nested graphs where scaling is maintained upon resizing. The lattice and ggplot2 packages use grid graphics to create more specialized graphing functions and methods. The spplot function for example is plot method built with grid graphics that you will use to create maps. The ggplot2 package is an implementation of the grammar of graphics combining advantages from the standard and lattice graphic environments. It is worth the effort to learn. We begin with the standard graphics environment. A box plot is a graph of the five-number summary. The summary function applied to data produces the sample mean along with five other statistics including the minimum, the first quartile value, the median, the third quartile value, and the maximum. The box plot graphs these numbers. This is done using the boxplot function.

Validation of microwave vegetation indices using field experiment data sets

2009 ◽  
Author(s):  
T. J. Jackson ◽  
J. C. Shi ◽  
R. Bindlish ◽  
M. Cosh ◽  
L. Chai ◽  
...  
Keyword(s):  
Field Experiment ◽  
Vegetation Indices ◽  
Data Sets ◽  
Experiment Data

Identification of Temporally Varying Areas of Interest in Long-Duration Eye-Tracking Data Sets

2019 ◽  
Vol 25 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Prithiviraj K. Muthumanickam ◽  
Katerina Vrotsou ◽  
Aida Nordman ◽  
Jimmy Johansson ◽  
Matthew Cooper
Keyword(s):  
Eye Tracking ◽  
Data Sets ◽  
Tracking Data ◽  
Long Duration ◽  
Areas Of Interest

scholarly journals GENERAL MODEL FOR ESTIMATION OF LONGSHORE TRANSPORT AT SHINGLE/MIXED BEACHES

2017 ◽  
pp. 26
Author(s):  
Giuseppe Roberto Tomasicchio ◽  
Felice D'Alessandro ◽  
Giuseppe Barbaro ◽  
Francesco Ciardulli ◽  
Antonio Francone ◽  
...  
Keyword(s):  
New Zealand ◽  
Field Experiment ◽  
General Model ◽  
Field Data ◽  
East Coast ◽  
Data Sets ◽  
Predictive Capability ◽  
Longshore Transport ◽  
Sand And Gravel ◽  
Gravel Beach

In the present study, the accuracy of the GLT model (Tomasicchio et al., 2013) has been verified for the estimation of the Longshore Transport (LT) at shingle/mixed beaches. In order to verify the suitability of the GLT model in determining LT estimates at shingle beaches, without any further calibration, the comparison between the LT predictions and observations from two field data sets (Chadwick, 1989; Nicholls and Wright, 1991) has been considered. The comparison showed that the GLT predicted LT rates within a factor of 2 of the observed values. The predictive capability of the GLT has been also verified against an alternative general formula for the LT estimation at shingle beaches (Van Rijn, 2014). In addition, the suitability of the GLT model, even for the mixed beach case, has been assessed by means of the comparison between the LT prediction and the observation from a field experiment on a mixed sand and gravel beach at Hawke’s Bay, on the east coast of New Zealand (Komar, 2010).

scholarly journals VisME: Visual microsaccades explorer

2019 ◽  
Vol 12 (6) ◽  
Author(s):  
Tanja Munz ◽  
Lewis L. Chuang ◽  
Sebastian Pannasch ◽  
Daniel Weiskopf
Keyword(s):  
Eye Tracking ◽  
Visual Analytics ◽  
Movement Direction ◽  
Data Sets ◽  
Tracking Data ◽  
Interactive Tools ◽  
Brushing And Linking ◽  
Parameter Values ◽  
Filter Algorithms

This work presents a visual analytics approach to explore microsaccade distributions in high-frequency eye tracking data. Research studies often apply filter algorithms and parameter values for microsaccade detection. Even when the same algorithms are employed, different parameter values might be adopted across different studies. In this paper, we present a visual analytics system (VisME) to promote reproducibility in the data analysis of microsaccades. It allows users to interactively vary the parametric values for microsaccade filters and evaluate the resulting influence on microsaccade behavior across individuals and on a group level. In particular, we exploit brushing-and-linking techniques that allow the microsaccadic properties of space, time, and movement direction to be extracted, visualized, and compared across multiple views. We demonstrate in a case study the use of our visual analytics system on data sets collected from natural scene viewing and show in a qualitative usability study the usefulness of this approach for eye tracking researchers. We believe that interactive tools such as VisME will promote greater transparency in eye movement research by providing researchers with the ability to easily understand complex eye tracking data sets; such tools can also serve as teaching systems. VisME is provided as open source software.

scholarly journals Imaging Time Series of Eye Tracking Data to Classify Attentional States

2021 ◽  
Vol 15 ◽  
Author(s):  
Lisa-Marie Vortmann ◽  
Jannes Knychalla ◽  
Sonja Annerer-Walcher ◽  
Mathias Benedek ◽  
Felix Putze
Keyword(s):  
Time Series ◽  
Eye Tracking ◽  
Eye Gaze ◽  
Image Features ◽  
Data Sets ◽  
Neural Net ◽  
Tracking Data ◽  
Attentional State ◽  
Gaze Behavior ◽  
Imaging Time

It has been shown that conclusions about the human mental state can be drawn from eye gaze behavior by several previous studies. For this reason, eye tracking recordings are suitable as input data for attentional state classifiers. In current state-of-the-art studies, the extracted eye tracking feature set usually consists of descriptive statistics about specific eye movement characteristics (i.e., fixations, saccades, blinks, vergence, and pupil dilation). We suggest an Imaging Time Series approach for eye tracking data followed by classification using a convolutional neural net to improve the classification accuracy. We compared multiple algorithms that used the one-dimensional statistical summary feature set as input with two different implementations of the newly suggested method for three different data sets that target different aspects of attention. The results show that our two-dimensional image features with the convolutional neural net outperform the classical classifiers for most analyses, especially regarding generalization over participants and tasks. We conclude that current attentional state classifiers that are based on eye tracking can be optimized by adjusting the feature set while requiring less feature engineering and our future work will focus on a more detailed and suited investigation of this approach for other scenarios and data sets.

scholarly journals Performance of Combined Recommendation Modeling Fused With Tagawareness In Mixed Traffic Scenarios

2021 ◽  
Author(s):  
Xiangyuan Li ◽  
Fei Ding ◽  
Suju Ren ◽  
Jianmin Bao ◽  
Ruoyu Su ◽  
...  
Keyword(s):  
High Speed ◽  
Recommendation System ◽  
Short Term Memory ◽  
User Behavior ◽  
Data Sets ◽  
Mixed Traffic ◽  
User Interest ◽  
Travel Information ◽  
Lstm Network ◽  
Behavior Characteristics

Abstract Due to the heterogeneous characteristics of vehicles and user terminals, information in mixed traffic scenarios can be interacted based on the Web protocol of different terminals. The recommendation system can dig users' travel preferences by analyzing historical travel information of different traffic participants, to publish accurate travel information and services for the terminals of traffic participants. The diversification of existing road network users and networking modes, as well as the dynamic changes of user interest distribution caused by high-speed movement of vehicles, traditional collaborative filtering algorithms have limitations in terms of effectiveness. This paper proposes a novel Hybrid Tag-aware Recommender Model (HTRM). The model embedding layer first employs the Word2vec model to represent the tags and ratings of projects and users, respectively. The feature layer then introduces the auto-encoder to extract self-similar features of the item, and a long short-term memory (LSTM) network is used to extract user behavior characteristics to provide higher-quality recommendations. The gating layer combines the features of users and projects and then makes score recommendations based on the Fully Connected Neural Network (FCNN). Finally, Web data sets of different service preferences of traffic participants during the trip are used to evaluate the model recommendation performance in different scenarios. The experimental results show that the HTRM model is reasonable in design and can achieve high recommendation accuracy.

Overview of Big Data and Its Visualization

2022 ◽  
pp. 22-53
Author(s):  
Richard S. Segall ◽  
Gao Niu
Keyword(s):  
Big Data ◽  
Traffic Safety ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Real Data ◽  
Flow Diagram ◽  
Data Sets ◽  
United States Department ◽  
Big Data Visualization ◽  
Challenges And Opportunities

Big Data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. This chapter discusses what Big Data is and its characteristics, and how this information revolution of Big Data is transforming our lives and the new technology and methodologies that have been developed to process data of these huge dimensionalities. This chapter discusses the components of the Big Data stack interface, categories of Big Data analytics software and platforms, descriptions of the top 20 Big Data analytics software. Big Data visualization techniques are discussed with real data from fatality analysis reporting system (FARS) managed by National Highway Traffic Safety Administration (NHTSA) of the United States Department of Transportation. Big Data web-based visualization software are discussed that are both JavaScript-based and user-interface-based. This chapter also discusses the challenges and opportunities of using Big Data and presents a flow diagram of the 30 chapters within this handbook.

Study on Driver’s Safety Sense Rule Based on Perception Speed at Daytime and Night

2012 ◽  
Vol 461 ◽  
pp. 128-131
Author(s):  
Li Xin Wu ◽  
Guo Zhu Cheng
Keyword(s):  
Field Experiment ◽  
Traffic Safety ◽  
Speed Limit ◽  
Rule Based ◽  
Driving Speed ◽  
Four Levels ◽  
Highway Alignment

In order to ensure traffic safety on freeway, speed limit is necessary. Driver’s safety sense to different speed is not same, so it should be considered by speed limit standard. Through field experiment, the data of driver’s perception speed at daytime and night were obtained. The data at daytime and night were compared. The deviations of perception speed were analyzed under different conditions of driving speed and highway alignment at daytime and night. Driver’s safety senses were classified as four levels of very safe, safe, dangerous and very dangerous. The relation between driver’s safety sense level and perception speed was analyzed. It shows that driver’s perception speed corresponding different safety sense level at daytime and night are also different.

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